The {Closed} Session

Most healthcare AI founders pitch the technology instead of patient outcomes. Colin Lawlor, founder of Sleep.ai with 15 years in sleep science and 250+ clinical studies, explains why sleep data requires specialized infrastructure beyond generic LLMs. He reveals how Sleep.ai's billion-hour proprietary dataset creates defensible moats through wearable normalization models, intervention efficacy mapping across 600+ devices, and closed-loop outcome measurement that health apps can't replicate without years of R&D investment.
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Most founders treat culture like an afterthought until it breaks. George Schlossnagle, founder of Message Systems (sold to MessageBird for $600M), explains why culture debt accumulates faster than technical debt and destroys scaling companies from within. He breaks down the "culture branching" phenomenon that happens around 100 employees, shares frameworks for detecting early warning signs like "that's not how we do things here," and reveals why hiring for cultural fit matters as much as technical capability in post-funding execution.
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Most enterprise AI delivers generic responses when you need role-specific intelligence. Peter Day, General Partner at super{set} with a PhD in machine learning and 8 years leading product at Quantcast, breaks down why agentic personalization creates unbreachable competitive moats. He reveals the architectural patterns for building software that learns individual preferences in real-time, explains why role collapse will eliminate 60-80% of traditional engineering positions, and details the shift from seat-based SaaS pricing to outcome-driven models that compound user value.
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Most enterprise AI failures aren't model problems—they're data architecture problems. Vivek Vaidya, serial entrepreneur with 25+ years building enterprise software and current CTO/Co-founder of super{set}, explains why vector databases alone can't solve enterprise AI and why knowledge graphs are foundational for production systems. He breaks down the critical difference between augmented intelligence (AI proposes, human approves) versus full automation, details how governance layers must respect existing enterprise data policies, and reveals why non-deterministic LLM outputs create compliance nightmares that kill enterprise adoption.
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Most financial markets still run on one-to-one matching when optimization engines could unlock trillions. Kelly Littlepage co-founded OneChronos after exits to major financial institutions, building the fastest-growing US equities market processing $20 billion daily using Nobel Prize-winning combinatorial auction theory. He breaks down why capital markets lagged behind ad markets in auction sophistication, how machine learning solves NP-hard matching problems at Wall Street speed, and why GPU compute markets need combinatorial auctions to handle non-fungible infrastructure across latency zones and chip architectures.
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